Skip to main content
Glama
Sign In
MCP CNPJ Intelligence

MCP CNPJ Intelligence

by raphaelmarra
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

buscar_similares

Find similar Brazilian companies using multi-dimensional scoring based on economic activity, capital size, and region to identify lookalike businesses for portfolio expansion.

Instructions

Encontra empresas SIMILARES usando scoring multi-dimensional.

QUANDO USAR:

  • Tem um cliente bom e quer encontrar mais parecidos (lookalike)

  • Quer expandir carteira com empresas do mesmo perfil

ALGORITMO DE SIMILARIDADE:

  • Mesmo CNAE (atividade economica)

  • Mesmo porte (capital social similar)

  • Mesma regiao

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "cnpj": "<cnpj>"
    },
    "name": "buscar_similares"
  }
}

Input Schema

NameRequiredDescriptionDefault
cnpjYesCNPJ da empresa de referencia
limiteNoQuantidade maxima de resultados (padrao: 50)
score_minimoNoScore minimo de similaridade 0-100 (padrao: 40)
ufNoFiltrar por estado (sigla 2 letras)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses the similarity algorithm (CNAE, size, region), which adds valuable behavioral context beyond basic functionality. However, it doesn't cover aspects like performance characteristics, error handling, or output format, leaving gaps in behavioral understanding for a tool with no annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (purpose, usage guidelines, algorithm), front-loaded with the core purpose. Every sentence adds value: the first states what it does, the second provides usage scenarios, and the third explains the similarity criteria. No wasted words or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description is moderately complete. It covers purpose, usage, and algorithm well, but lacks details on output format, error cases, or performance limits. For a tool with 4 parameters and complex similarity matching, more behavioral context would be beneficial to fully guide the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds meaningful context by explaining the multi-dimensional scoring approach (CNAE, capital, region), which helps interpret the 'score_minimo' parameter's significance. This elevates the score above baseline, though it doesn't provide detailed syntax beyond what the schema offers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Encontra') and resource ('empresas SIMILARES'), distinguishing it from siblings like 'buscar_empresa' (find specific company) or 'buscar_por_cnae' (find by activity code). It specifies 'scoring multi-dimensional' for similarity matching, making the purpose distinct and well-defined.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'QUANDO USAR' section explicitly provides two scenarios for when to use this tool: for lookalike analysis of good clients and for portfolio expansion with similar companies. This gives clear, actionable guidance that differentiates it from alternatives like 'benchmark_empresa' or 'buscar_avancado' without needing to list exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools